{Keith C.C. Chan Gong Chen Xiang Zhang Yan Liu Bruce X.B. Yu}

Abstract

Recently, some skeleton-based physical therapy systems have been attempted to automatically evaluate the correctness or quality of an exercise performed by rehabilitation subjects. However, in terms of algorithms and evaluation criteria, the task remains not fully explored regarding making full use of different skeleton features. To advance the prior work, we propose a learning framework called Ensemble-based Graph Convolutional Network (EGCN) for skeleton-based rehabilitation exercise assessment. As far as we know, this is the first attempt that utilizes both two skeleton feature groups and investigates different ensemble strategies for the task. We also examine the properness of existing evaluation criteria and focus on evaluating the prediction ability of our proposed method. We then conduct extensive cross-validation experiments on two latest public datasets: UI-PRMD and KIMORE. Results indicate that the model-level ensemble scheme of our EGCN achieves better performance than existing methods. Code is available: https://github.com/bruceyo/EGCN.